Quantitative Construction Research: A Paradigm Shift from Traditional Statistical Techniques to AI?

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Quantitative Construction Research:

A Paradigm Shift from Traditional Statistical Techniques to AI?

Goh Bee Hua

Publication details:

Goh, B.H. (2008), “The state of applications of quantitative analysis techniques to construction economics and management (1983 to 2006)”, Construction Management and Economics, Taylor & Francis, 26(5), 485-497.

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Abstract

The use of artificial intelligence (AI) methods, such as artificial neural networks (ANNs), in the

construction field has been in experimentation since the evolution of one of the most important ANN

paradigm known as Backpropagation in the mid-eighties. This paper presents a review of studies that

have applied biological-based AI methods, such as genetic algorithms (GAs) and ANNs, to the

simulation and optimization of construction processes, and the modelling and forecasting of

construction variables such as tender bids, demand and cost, respectively. Some of the highlighted

works include the use of ANNs and GAs to solve complex construction site operational problems; to

identify the most appropriate construction method, plant combination and labour allocation, so as to

optimize costs, time and production; to examine the relationships between variables in the

construction bidding process for the purpose of predicting future tender bids; to model the complex

nonlinear relationship between demand for construction and its influencing economic factors; and to

model and estimate construction cost using subjective or 'noisy' data. The key benefits cited include a

more efficient method of simulating construction activity owing to the parallel processing of

information; a more effective search and optimization approach that can quickly locate high

performance regions in extremely large and complex search spaces; the building of more complex

nonlinear models to produce realistic and accurate forecasts; and the availability of techniques that

can perform nonlinear modelling and adaptation automatically without the need for functional

assumptions. For quantitative research in the field of construction management and economics, the

review serves to formalise the transition from traditional statistical techniques to those that are AI-

based. It is timely to acknowledge a paradigm shift in quantitative construction research so that

researchers can better prepare themselves for the future by acquiring new skills and knowledge.

Keywords: artificial neural networks, statistical techniques, quantitative research,

construction management, construction economics

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Introduction

The construction industry is most often associated with complexity; in terms of the nature of work,

management of processes, organization of parties’ relationships, and including its inter-relationship

with the rest of the economy. In dealing with such complexities, conventional decision-analysis

techniques were regarded as less acceptable by practicing construction personnel, especially when

they involve advanced mathematics such as probabilistic methods and multi-attribute utility theory

(Ahmad and Minkarah, 1990). Levitt (1987) highlighted that the key requirements for the resolution

of construction problems, as well as the performance of construction management tasks are expert

knowledge, judgment and experience. Many expert system applications in construction-related areas

have been made on this basis and the intention is for the system to attempt to model the intelligent

reasoning and problem-solving capabilities of the human brain. However, as neural networks

research started to flourish in the mid eighties, it was realized that expert systems lack the ability to

learn by themselves, generalize solutions and adequately respond to highly correlated, noisy,

incomplete or previously unseen data (Wassermann, 1989; Pao, 1989). In contrast, neural networks

have these abilities by attempting to model the brain learning, thinking, storage and retrieval of

information, as well as associative recognition (Moselhi et al., 1991). Successful applications of

neural networks in the field of construction management and economics have stimulated further

interest in exploring the capabilities of other biological-based methods such as genetic algorithms.

These are parallel, adaptive search algorithms inspired by the mechanisms of biological evolution,

and have been used to solve a variety of optimization problems (Goldberg, 1989). So far, they have

either been used alone to solve construction optimization problems or in combination with neural

networks to provide a synergy with more problem-solving power. In essence, neural networks mimic

brain functions while genetic algorithms mimic the process of evolution and they both have a

common motivation, that is, to learn from nature’s efficiency.

This paper presupposes a paradigm shift from the use of traditional statistical approaches to

biological-based AI techniques in construction management and economics research. Hence, it is

seen to be timely to present a review of past and recent studies in order to formalize this transition so

that researchers can better prepare themselves for the future by acquiring new skills and knowledge.

The paper focuses on two key areas of construction management and economics research where the

paradigm shift has been most predominant. They are in the areas of modelling and forecasting of

construction variables such as tender price, construction demand and cost, and the simulation and

optimization of construction processes. It also serves the purpose of narrowing down the scope of the

review as the field of construction management and economic is inherently broad and diverse.

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Following the review, the causes for the shift are discussed and the general performance of these

biological-based AI methods is evaluated to see if the transition has indeed made positive impact on

this field of research.

Modelling construction economics variables using traditional approaches

In the construction industry, regression techniques have often been used to model and forecast

construction variables owing to their relative simplicity in both concept and application. In essence,

regression techniques are concerned with the modelling of relationships among variables and they

quantify how a response variable is related to a set of explanatory variables. Univariate time-series

methods are also popular, especially when there is lack of information on such explanatory variables,

and the forecast has to rely on identifying and extrapolating the underlying pattern of the historical

data from the series itself. In essence, time-series forecasting treats the system as a black box and

makes no attempt to discover the factors affecting its behaviour. In support of Taylor and Bowen’s

(1987) suggestion, Wang and Mei (1998) acknowledge that there are two categories of quantitative

forecasting: (1) the causal method and (2) the time-series method.

In the domain of tender bid and price forecasting, both regression and time-series approaches have

been applied. Carr and Sandahl (1978) applied multiple regression analysis to competitive bidding to

provide decision support on whether to bid or not to bid and to estimate the bid markup. Flanagan

and Norman (1983) used simple linear and curvilinear regression analyses to establish the relationship

between quantity surveyor’s price prediction made at the design stage and the low bids received from

contractors. While McCaffer et al. (1983) noted that higher levels of accuracy can be achieved for

tender price forecasting using time-series methods, Taylor and Bowen (1987) adopted a hierarchy of

time-series forecasting methods, ranging from simple moving average and single exponential

smoothing to advanced decomposition and Box-Jenkins techniques, to forecast future building price

index levels. However, Runeson (1988) still relied on multiple regression analysis to quantify the

effects on the price level of variations in the degree of competitiveness in the industry, using Market

Conditions Index as the dependent variable. Fellows (1987 and 1991) used the Box-Jenkins

methodology, in particular, to predict building cost and tender price indices with the objective of

managing escalation by forecasting the effects of inflation in the UK building industry. Adopting the

same approach, Goh (1995) examined the effects of inflation on public industrial projects in

Singapore via tender price index forecasting.

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Similarly, these conventional approaches have been used to model and forecast contractors’ workload

or, in broader terms, demand for construction. A comparative study was undertaken to evaluate the

forecasting accuracy of the regression and time-series methods, using the Box-Jenkins technique as

the benchmark (Oshobajo and Fellows, 1989). Other works relied mainly on regression analyses,

such as, Killingsworth Jr. (1990) developing a demand forecasting model based on a regression

analysis of the leading indicators of industrial construction in the US, and Tang et al. (1990) applying

regression analyses to project values of total construction activities for the Thai construction industry.

In addition, models of UK private-sector quarterly construction demand were developed by Akintoye

and Skitmore (1994) using a priori selected leading indicators and applying the regression technique

with lagged terms. More recently, Goh (1998) compared the accuracy of the Box-Jenkins and the

multiple regression techniques in forecasting residential construction demand in Singapore. Owing to

the extensive use of regression analyses in demand forecasting, Goh (1999) extended her study to

explore the accuracy of both linear and nonlinear regression methods applied to modelling

Singapore’s residential, industrial and commercial construction demand.

For construction cost estimation and prediction, the conventional approaches adopted have also not

departed from causal and time-series methods. As a cost estimating tool, McCaffer (1975) illustrated

with examples the general use of regression analysis. Specifically for reinforced concrete framed

structures, Bowen (1982) examined the feasibility of econometric cost modelling as an alternative

estimating approach. On the prediction of construction costs for public school buildings in Jordan,

Al-Momani (1996) developed an empirical model by experimenting the use of linear and loglinear

regression methods. For assessing the variations in construction labour and material costs, Wang and

Mei (1998) presented a new approach, based on time-series forecasting concepts, to predict

construction cost indices in Taiwan.

Modelling construction management variables using traditional approaches

In the construction management process, the key consideration is to use available resources in the most

efficient manner in order to minimise time and cost with the ultimate goal of enhancing productivity and

cash-flow of a project. Past studies have ranged from the application of deterministic critical path

methods such as CPM and PERT to the adoption of stochastic or probabilistic approaches such as

Monte Carlo simulation and related optimisation techniques. Cusack (1984) outlined the historical

development of mathematical models in the planning and control of construction projects and discussed

the limitations of such deterministic techniques. He stressed the need to model and inter-relate time cost

parameters in a dynamic way through simulation and optimisation. In concurrence, Bennett and

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Ormerod (1984) re-emphasised the claim that the introduction of simulation methods for construction

management is likely to have as great an impact on the construction industry as did the introduction of

network planning and scheduling methods some two decades ago. In essence, simulation is a technique

which is employed to form a management tool in an uncertain environment in order to augment the

decision maker’s intuition and experience. Stochastic simulation typically generates durations and costs

for each activity in a plan by randomly calculating a feasible value for each from a statistical probability

frequency distribution which represents the range and pattern of possible outcomes for an activity. With

the use of computer-based simulation, studies can be undertaken to determine the most appropriate

construction method, plant combination and labour allocation, so as to optimise costs, time and

production, and to predict probable project durations, costs and production for estimating and tendering

purposes (Flood, 1990).

Owing to the diversity of construction work and range of methods and resources employed, computer

simulation and optimisation methods have dominated research studies for solving complex construction

operational problems. However, based on mathematical formulation of optimum rates the concreting

and formwork operation, Gates and Scarpa (1978) derived formulas to optimise the rate of pour that

dictates formwork design, the number of uses of formwork, and the number and height of lifts.

Customised simulation tools have also been developed for specific construction operations such as the

SCRAPESIM for earth-moving owing to the cost significance of this activity where inaccuracies in

estimating may greatly influence the profit margin of an earth-moving contractor (Clemmens and

Willenbrock, 1978). For solving large-scale operational problems, interactive man-computer graphics

systems and heuristic modelling approaches for simulation have been proposed (Paulson Jr., 1978).

Different applications of stochastic techniques to simulate construction activity have been described by

Pilcher and Flood (1984) and their review of previous and current research serves to acknowledge the

potentially usefulness of computer-based simulation for construction managers in supplementing

intuition and expertise. To further enhance existing simulation tools for construction operations,

Paulson et al. (1987) proposed a system that combines videotape data collection from field construction

operations, statistical reduction and analysis of data, and computer-based simulation modelling. More

recent research (Alkoc and Erbatur, 1998) extends the boundary of simulation models to include a

sensitivity analysis of the concreting operations to a set of possible resource combinations.

For construction resource estimation, mainly dealing with time and cost, the use of stochastic simulation

techniques has been prevalent. Carr (1979) presented a simulation model for uncertainty determination

of the timing of each construction activity, even when the activity durations are not independent of each

other. As an integrated approach, Woolery and Crandall (1983) proposed the use of a stochastic

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network model, based on Monte-Carlo simulation, to model the dependencies between network

activities for construction scheduling. As a means to augment pre-contract planning for large and

complex projects or for re-planning work in progress, the use of Monte-Carlo simulation was also

proposed for developing construction resource models (Baxendale, 1984). A suite of computer

programmes, known as the Construction Project Simulator, was developed to generate simulation

outputs for duration and cost predictions, and cash flow curves, based on costs, resources, weather and

productivity data (Bennett and Ormerod, 1984). Considering mathematical approaches as complex

solutions to construction resource modelling, Cusack (1985) presented a simplified approach to the

planning and control of cost and project duration by applying heuristics to model the time-cost

relationship. As a major addition to the repertoire of existing computer-based project scheduling tools,

Ahuja and Nandakumar (1985) developed a comprehensive model to simulate and combine the impact

of the significant uncertainty variables, such as weather, space congestion, workmen absenteeism, and

incorporates it in the activity duration estimates as it revises them. Further research developments in the

area of project scheduling include the development of a systematic methodology for stochastic

scheduling involving eight steps (Karni, 1987) and the formulation of a linear integer model for

optimisation of project schedules to achieve the primary objective of minimising total project cost

(Karshenas and Haber, 1990).

Research studies on the estimation and prediction of construction cash flow typically dealt with

generating alternative forms of the S-curve. The techniques applied ranged from mathematical

formulation of the S-curve to stochastic simulation of the patterns of construction project expenditure.

As a general approach, Tucker (1986) introduced the mathematical formulation of construction cash

flow curves using an analogy between the probability of failure in reliability theory and the probability

of payment during construction. It permits the exploration of any function to define the payment

completion rate from which the cumulative payment and payment density functions can be derived.

More specifically for the post hoc examination of construction project net cash flows, Kenley and

Wilson (1989) proposed a model based upon the logit transformation which was found to be very

flexible and capable of adapting to the wide degree of inter-project variability. In contrast with

deterministic approaches, simulation of expenditure patterns of construction projects to analyse and

examine their shapes was proposed by Khosrowshahi (1991) as a means of facilitating mathematical

models for ease of application, user involvement and user comprehension. To further improve on

current standard value S-curves, Kaka and Price (1991) focused their attention on cost commitment

curves instead, especially to help contractors generate the value and cash-out curves at the tendering

stage. The applicability of such curves was tested on several project classification criteria and curve-

fitting was carried out using the logit transformation technique (Kaka and Price, 1993).

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Similarly, the types of techniques applied in construction productivity studies ranged from deterministic

modelling to stochastic simulation. An interactive system for the analysis of construction operations by

integrating quantity development and process simulation to estimate productivity and cost was proposed

by Tavakoli (1985). By applying the learning curve theory to construction productivity, five basic

mathematical models for learning curves, ranging from straight-line to exponential forms, were

evaluated on estimating and predictive abilities (Thomas et al., 1986). Thomas and Yiakoamis (1987)

developed a factor model using multiple regression techniques to mathematically explain variability

caused by the effects of temperature and relative humidity in the daily productivity data.

Equipment selection is also an important consideration in construction management and research studies

often apply simulation and optimisation techniques to experiment different combinations of

construction plant and equipment to improve site productivity or select the least cost option. Woods and

Harris (1980) developed a truck allocation model for concrete distribution by applying computer-based

simulation to obtain the most suitable combination of trucks in the fleet in order to minimise operating

costs and reduce waste space from part loads. However, mathematical techniques were relied upon to

propose an algorithm which can define the least expensive cranage cost for a project based on varying

the type and number of cranes used during construction (Furusaka and Gray, 1984). Both queuing

theory and simulation techniques were adopted by Touran and Taher (1988) to develop a model that can

predict the productivity, as well as determine the optimum fleet size using sensitivity analysis, of the

earth-moving operation. For materials handling, Wijesundera and Harris (1989) developed a dynamic

interactive simulation model to allow for varying crane type, size and location, skip size, delivery

system and construction crew size to evaluate the effects on utilisation levels and costs for different

methods of working.

Biological-based AI applications in construction management and economics

The use of AI, namely expert systems, in the field of construction management and economics began in

the early eighties. Brandon (1990) reviewed the historical development of this AI application in the

construction industry, highlighting its potential and limitations. With knowledge acquisition being the

major bottleneck in the development of expert systems (Bowen and Erwin, 1990), other biological-

based applications started to evolve in this field. Since the early nineties, artificial neural networks

(ANNs) have become popular, especially in the area of construction management. Their superiority

over expert systems stems from the nature of construction problems which is of pattern recognition

rather than deep reasoning about the problem elements (Moselhi et al., 1991). More recently, genetic

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algorithms (GAs), another biological-based method, are gaining recognition in solving construction

optimisation problems either alone or in combination with ANNs.

In the complex decision-making process of competitive bidding, ANNs have been applied to bid

prediction and analysis, and mark-up estimation. Gaarslev (1991) developed a neural network to predict

the outcome of a future bidding situation based on historical data such as number of bidders, estimated

cost, percentage of work not sub-contracted and project duration. Although McKim (1993) adopted the

same set of data for his study, he extended his objective to include a comparison of the predictive

capabilities of a traditional statistical methods and ANNs. To aid contractors in preparing competitive

bids for building projects, Moselhi et al. (1993) developed a decision-support system which uses ANNs

for mark-up estimation and the Monte Carlo simulation technique for assessing the contractor’s

assessment of project risks. Focusing only on optimum mark-up estimation, Hegazy and Moselhi

(1994) proposed an analogy-based solution, which combines ANNs and GAs, to mark-up estimation

problem, while Li (1996) compared the performance of ANNs to a regression-based method and

identified the effect of different ANN configurations, such as the number of hidden layers and nodes in

each hidden layer, on estimating accuracy. Further improvements on the ANN methodology have been

undertaken on construction mark-up estimation (Li et al., 1999).

For modelling demand for construction, comparative studies have been carried out to evaluate the

forecasting accuracy of ANNs against traditional techniques such as regression and time-series analyses.

Adopting linear and nonlinear forms, Goh (1996) developed two residential construction demand

models using multiple linear regression and the backpropagation ANN, respectively. Further

developments of this comparison include an evaluation of the backpropagation ANN against multiple

loglinear regression and the univariate Box-Jenkins approach (Goh, 1998), and against the performance

of combining ANNs and GAs (Goh, 2000).

For solving complex construction operational problems, the use of ANNs has also been explored

(Flood, 1990a). An ANN approach was proposed by Flood (1989) to achieve an optimal sequencing of

construction tasks with the aim of minimising production time. Based on the efficient solutions

obtained in the study to the sequencing problem, he concluded on the possibility of applying ANNs to

other types of operational problems such as resource allocation, material cutting and site layout. Flood

(1990b) also illustrated with the use of a conceptual ANN model to simulate a construction process by

linking network modules, representing a queuing facility, storage facility or productive resource, to

design a complete network description.

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As time and cost are two important inter-related factors to be planned and controlled in construction

projects, the capabilities of both ANNs and GAs have been explored. A judgment-based forecasting

approach, using multiple regression techniques and ANNs, was proposed by Al-Tabtabai et al. (1997) to

identify schedule variances from a baseline plan for typical construction projects. It essentially uses

ANNs to capture the complex nonlinear decision-making process of project experts involved in

schedule monitoring and prediction. For time-cost optimisation problems in construction planning, Li

and Love (1997) evaluated the performance of a basic GA system and implement modifications to the

crossover and mutation operations. Besides outperforming the basic system, the improved GA system

was found to be able to generate a whole class of alternative solutions close to the optimum. On the

other hand, Adeli and Karim (1997) adopted ANNs for solving the nonlinear optimisation problem for

construction project scheduling. By varying the construction duration, one can solve the cost-duration

trade-off problem, and obtain the global optimum schedule and the corresponding minimum

construction cost.

Similarly for studies on construction budget performance and cost estimation, both ANNs and GAs

have been applied. A neural network approach was used to identify the key management factors that

affect budget performance in a construction project and develop a prediction model based on their

complex relationships (Chua et al., 1997). Adeli and Wu (1998) formulated the regularisation neural

network for construction cost estimation by incorporating a regularisation term in the error function to

compensate for the overfitting problem and improve estimation outside the scope of available data

points. The performance of this new computational model depends only on the training examples; and

not on the architecture of the network, learning parameters and number of training iterations. Hegazy

and Ayed (1998) also used ANNs for their study on parametric cost estimation but relied on simplex

optimisation and GAs to determine the network weights. Accordingly, the weights that produced the

best cost prediction for the historical cases were used to find the optimum network. For industry-level

cost prediction, William (1994) developed a backpropagation ANN model to predict the changes in

construction cost indexes using related economic indicators as model inputs. The performance of the

ANN model was compared with the exponential smoothing and linear regression models.

In construction productivity studies, ANNs have been used to estimate operation productivity, as well as

labour productivity. Chao and Skibniewski (1994) adopted a neural-network and observation-data-

based approach to estimate the production rate for the excavation and hauling operation. Experimenting

on a different construction operation, Portas and AbouRizk (1997) developed a three-layered ANN with

a fuzzy output structure to estimate construction productivity for concrete formwork tasks. For

modelling construction labour productivity, Sonmez and Rowings (1998) adopted a methodology based

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on regression and ANNs techniques to develop prediction models for concrete pouring, formwork and

concrete finishing tasks. It has presented an approach for the evaluation of the impact of multiple

factors, considered simultaneously, on labour productivity.

For the selection of construction equipment, studies have adopted GAs and ANNs to search for an

optimum equipment selection, and classify new and obsolete equipment based on their production rates,

respectively. To classify earthmoving equipment according to the speed of operation, Karshenas and

Feng (1992) used ANNs as a modular approach to facilitate the inclusion and removal of new and

obsolete equipment considered by the network. On the other hand, for the optimum selection of

excavating and haulage equipment in opencast mining, Haidar et al. (1999) designed a decision-support

system using a hybrid knowledge-based system and GAs. The knowledge base relates mainly to the

selection of equipment in broad categories while the advanced GAs search techniques find the input

variables that can achieve the optimal cost.

Discussion on the paradigm shift

The contentious issue to be decided here is whether there has been a paradigm shift. In order to explore

this issue further, two vital questions have to be addressed. They are: (1) if there has been no paradigm

shift, is there a need for one?; and (2) if a paradigm shift has taken place, is it effective in terms of

having improvements over traditional methods based on the existing paradigm?

The review of studies having applied traditional and biological-based AI techniques has been

summarised in Tables 1 and 2 for variables relating to construction economics and construction

management, respectively. Some studies had carried out a comparison of the performance of traditional

and biological-based AI techniques and the results have also been included. It is obvious that most of

the reviewed studies, i.e. 4 out of 5, have found AI techniques to be more accurate for the construction

economics forecasting studies. The superiority of non-traditional techniques over conventional ones has

been acknowledged in some of the construction management studies, even though no formal

comparison had been undertaken. This simple analysis can help to shed some light on the two

fundamental questions to be addressed here.

Firstly, if there has been no paradigm shift, is there a need for one? Bowen and Edwards (1985, p. 203)

believe that “pressure to adopt a new paradigm presupposes some failure of or dissatisfaction with the

existing one”. Based on the review, it is noted that there has been an increasing number of studies

experimenting with biological-based AI techniques in the field of construction economics and

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management. Hence, can this be regarded as a signal for at least some failure or dissatisfaction with

conventional techniques based on the existing paradigm. The comparative studies have specifically

used accuracy as the performance criterion for evaluating the modelling techniques. Does this mean

that the existing paradigm has been unable to fulfil the basic requirement of a quantitative model, that is,

to generate accurate forecasts?

Secondly, if a paradigm shift has taken place, is it effective in terms of having improvements over

traditional methods? The obvious answer for accuracy improvements is ‘yes’ for studies on modelling

construction economics variables. Time improvement has been cited in one of the construction

management studies (Flood, 1990b). The common explanation provided by many researchers on the

success of ANNs has been their inherent ability to learn and generalise from previous examples, and

they are particularly suitable for solving nonlinear and multivariate problems commonly found in the

domain of construction economics and management. Traditional methods such as regression and

simulation would require researchers to ascribe functional forms to fit the data patterns and specify

probability distributions for the variables concerned, respectively. Very often, these tasks are carried out

with little technical knowledge on the part of the researcher and judgmental input is heavily relied upon.

On the contrary, they could have been automatically handled using ANNs and GAs, eliminating the

element of subjectivity. Hence, in this case, is the subjectivity element inherent in conventional

methods the cause for the hypothesized failure or dissatisfaction with techniques based on the existing

paradigm?

Model specification has always been a problem in econometric modelling. In order to avoid

encountering the problem of having to ascribe a functional form to complex non-linear relationships,

the general research strategy has been to formulate a provisional model on the basis of economic

theory, experience and intuition, and then locate suitable data and fit the model. Dougherty (1992)

criticised this approach, explaining that the final version of the model would be satisfactory because

its specification would have been skilfully massaged to fit the particular data set, not that it really

corresponds to the true model. Besides, such judgmental interventions of the modeller would have

introduced personal biasness, further undermining the validity of such models.

However, the use of regression techniques in econometrics allows causal analysis to be carried out to

explain the relationships between dependent and independent variables. The coefficient estimates and

the various established significance tests allow the extent of the relationships to be clearly defined. In

contrast, ANNs lack this capability as they are not able to assess the relative importance of the input

factors used by the model to arrive at its conclusion. The absence of this explanatory power in ANNs

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has traditionally classified them as "black boxes" whose complex inner workings somehow transform

inputs into predicted outputs. Traditionally, they are not designed to exploit existing expertise;

instead they serve as highly intensive computational machines, deriving their ability to forecast

through a long learning process based on pre-defined sets of input-output values.

In the search for a new paradigm in cost modelling and price forecasting, Bowen and Edwards (1985, p.

207) commented that “what is needed is an approach which will harness the strengths and minimise the

weaknesses of all that has been developed to date, if the wasteful step of discarding the work of the last

fifteen years is to be avoided.” Similarly, if the use of AI techniques is the way forward for construction

management and economics modelling research and, hence, acknowledged as the new paradigm, the

inherent shortcomings of such methods need to be looked into carefully. Apparently, work in this

direction has begun since the early nineties when Garson (1991) developed a simple and intuitive

method for interpreting the interconnection weights in order to understand the impact various input

processing elements have on the output processing element as a means to eliminate the black-box

image of ANNs. More recently, work has been done to extract rules from neural networks (Fu,

1994). The main intention is to formalize the relationship between a neural network and a rule-based

system. On this basis, several studies have been undertaken to support construction mark-up

estimation (Hegazy and Moselhi, 1994; Li, 1996) as it is often regarded to be the most important and

difficult decision for a contractor when making a bid for a project since the mark-up estimate has to

be low enough to win the contract but high enough to make a profit. Further development includes

investigating the use of a rule-extraction method, known as KT-1, to automatically formulate rules for

the trained ANN instead of having to craft the rules manually (Li et al., 1999).

Bowen and Edwards (1985, p. 200) also suggest that “the need for a new paradigm is perceived as being

entirely an academic one”, that is to say, “pressure to consider a new paradigm emanates almost entirely

from an academic pursuit of knowledge.” They were in the view that although academics may see the

need as achieving a significant milestone, it holds little interest for practitioners. On this note, it would

be useful to highlight that the rapidly growing interdisciplinary field of nonlinear modelling and

forecasting has been due largely to the increasing availability of computer resources which allow for the

collection of increasingly large data sets and the analysis of data sets with numerically intensive

algorithms. Casdagli and Eubank (1991) attributed the growth in the field to the increasing recognition

of the ubiquity and importance of the effects of nonlinear dynamics in the natural and social sciences.

Among a wide range of disciplines working on different aspects of nonlinear modelling, advancement

in AI techniques has been one of the greatest. Hence, if it was true that the need for a shift had arisen

from an academic pursuit of knowledge, then it would have to be seen as a step in the positive direction

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of exploring the use of more computationally rigorous techniques that can model real-world situations

more exactly. With regard to modelling techniques that rely on manual specification of functional

forms, Newbold and Bos (1990) stressed that it would be quite unrealistic to hope that any simple model

and associated assumptions can provide a perfectly accurate description of the complexity that exists in

the real world. On the basis of testing out state-of-the-art methods, it is natural for research and

development work to stem from the academic arena with no significant role for practitioners to play at

this developmental phase. The anticipated challenge in the future would be to convince the industry on

the viability of such biological-based AI modelling techniques, in terms of their ability to generate good

quality forecasts with time and cost savings. In this way, the ideal link between academic institutions

and the industry can be further established with sustainability.

Table 1. A summary of studies on modelling construction economics variables

Construction Economics Variables

Conventional Techniques Applied

Biological-based Techniques Applied

Remarks

Tender Bids / Price

Carr & Sandahl (1978) Flanagan & Norman (1983) McCaffer et al. (1983) Taylor & Bowen (1987) Fellows (1987) Runeson (1988) Fellows (1991)

Gaarslev (1991) McKim (1993) * Moselhi et al. (1993) Hegazy & Moselhi (1994) Li (1996) * Li et al. (1999)

Comparative studies (*) have found ANNs to be superior to regression analysis in terms of forecasting accuracy.

Demand

Oshobajo & Fellows (1989) Tang et al. (1990) Killingsworth Jr. (1990) Akintoye & Skitmore (1994) Goh (1999)

Goh (1996) * Goh (1998) * Goh (2000)

Comparative studies (*) have found ANNs to be superior to multiple linear and loglinear regression, and comparable to the Box-Jenkins approach, which is a univariate time-series technique, in terms of forecasting accuracy.

Cost

McCaffer (1975) Bowen (1982) Al-Momani (1996) Wang & Mei (1998)

Williams (1994) *

The comparative study (*) has shown that ANNs did not outperform the exponential smoothing and simple linear regression techniques, in terms of forecasting accuracy.

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Table 2. A summary of studies on modelling construction management variables

Construction Management Variables

Conventional Techniques Applied

Biological-based Techniques Applied

Remarks

Operations

Gates & Scarpa (1978) Paulson Jr. (1978) Clemmens & Willenbrock (1978) Pilcher & Flood (1984) Paulson et al. (1987) Alkoc & Erbatur (1998)

Flood (1989) Flood (1990a) Flood (1990b) *

No formal comparative studies have been conducted but it has been said in a study (*) that ANNs have dramatic improvement over conventional computer simulation implementations in terms of the amount of simulation time incurred.

Resources (in relation to time & cost)

Carr (1979) Woolery & Crandall (1983) Baxendale (1984) Bennett & Ormerod (1984) Cusack (1985) Ahuja & Nandakumar (1985) Karni (1987) Karshenas & Haber (1990)

Al-Tabtabai et al. (1997) * Li & Love (1997) Adeli & Karim (1997)

No formal comparative studies have been conducted but it has been acknowledged in a study (*) that multiple regression is suitable for modelling linear relationships while ANNs are more flexible in capturing non-linearity.

Project Budget / Cash-flow

Tucker (1986) Kenley & Wilson (1989) Khosrowshahi (1991) Kaka & Price (1991) Kaka & Price (1993)

Chua et al. (1997) * Adeli & Wu (1998) Hegazy & Ayed (1998)

No formal comparative studies have been conducted but it has been highlighted in a study (*) that the ANN model has the ability to give good predictions even with previously unseen data and incomplete information.

Productivity

Tavakoli (1985) Thomas et al. (1986) Thomas & Yiakoamis (1987)

Chao & Skibniewski (1994) Portas & AbouRizk (1997) Sonmez & Rowings (1998) *

No formal comparative studies have been conducted but a study (*) has applied multiple regression and ANNs where the former was used to select significant inputs for the latter. The study has concluded that ANNs have shown potential for quantitative evaluation of the effects of multiple factors on productivity, especially when interactions and nonlinear relations are present.

Equipment Selection

Woods & Harris (1980) Furusaka & Gray (1984) Touran & Taher (1988) Wijesundera & Harris (1989)

Karshenas & Feng (1992) Haidar et al. (1999)

No comparative studies have been conducted.

16

Conclusions

Two vital questions have been addressed in the discussion. They are:

1) If there has been no paradigm shift, is there a need for one?

The review has revealed that there has been an increasing number of studies experimenting with

biological-based AI techniques in the field of construction economics and management. This could be

regarded as a signal for at least some failure or dissatisfaction with conventional techniques based on the

existing paradigm. Failure of the existing paradigm may be due to the inability of traditional techniques

to produce quantitative models that fulfil the basic requirement of generating accurate forecasts.

2) If a paradigm shift has taken place, is it effective in terms of having improvements over traditional

methods based on the existing paradigm?

The review has shown that, in terms of accuracy, improvements have been acknowledged in a large

proportion of construction economics studies. Time improvement has been cited in one of the

construction management studies. It has been questioned if subjectivity inherent in conventional

methods, in terms of requiring researchers to ascribe functional forms or specify probability

distributions to data patterns or modelling variables respectively, the cause for the hypothesized failure

or dissatisfaction with techniques based on the existing paradigm.

This has prompted a general discussion on the strengths and weaknesses of conventional and state-of-

the-art techniques. Based on the rationale that “what is needed is an approach which will harness the

strengths and minimise the weaknesses of all that has been developed to date, if the wasteful step of

discarding the work of the last fifteen years is to be avoided”, it has been concluded that the way

forward for construction management and economics modelling research is to explore how biological-

based AI techniques could be further enhanced to incorporate explanatory capability. A formalized

relationship between a neural network and a rule-based system needs to be established for future

research.

On the issue if “the need for a new paradigm is perceived as being entirely an academic one”, it has

been concluded that even if it is, it would have been in the right direction of exploring, as well as

exploiting, the use of more computationally rigorous techniques that can model real-world situations

more accurately. It has been concluded that the ideal link between academic institutions and the

industry could then be established, with sustainability, if research and development work could lead to

17

helping the industry adopt techniques that bring about time and cost savings, and in this case, biological-

based AI models that produce good quality forecasts.

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Quantitative Construction Research: A Paradigm Shift from Traditional Statistical Techniques to AI?

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